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Bayesian modeling of bacterial growth for multiple populations

Bayesian modeling of bacterial growth for multiple populations

Palacios, Ana Paula, Marín, J. Miguel, Quinto, Emiliano J. and Wiper, Michael P. (2014) Bayesian modeling of bacterial growth for multiple populations. The Annals of Applied Statistics, 8 (3). pp. 1516-1537. ISSN 1932-6157 (Print), 1941-7330 (Online) (doi:https://doi.org/10.1214/14-AOAS720)

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Abstract

Bacterial growth models are commonly used for the prediction of microbial safety and the shelf life of perishable foods. Growth is affected by several environmental factors such as temperature, acidity level and salt concentration. In this study, we develop two models to describe bacterial growth for multiple populations under both equal and different environmental conditions. Firstly, a semi-parametric model based on the Gompertz equation is proposed. Assuming that the parameters of the Gompertz equation may vary in relation to the running conditions under which the experiment is performed, we use feed forward neural networks to model the influence of these environmental factors on the growth parameters. Secondly, we propose a more general model which does not assume any underlying parametric form for the growth function. Thus, we consider a neural network as a primary growth model which includes the influencing environmental factors as inputs to the network. One of the main disadvantages of neural networks models is that they are often very difficult to tune which complicates fitting procedures. Here, we show that a simple, Bayesian approach to fitting these models can be implemented via the software package WinBugs. Our approach is illustrated using real experimental Listeria Monocytogenes growth data.

Item Type: Article
Uncontrolled Keywords: Bacterial population modeling, Growth functions, Neural networks, Bayesian inference
Subjects: Q Science > QA Mathematics
Faculty / Department / Research Group: Faculty of Architecture, Computing & Humanities
Faculty of Architecture, Computing & Humanities > Department of Mathematical Sciences
Last Modified: 31 May 2017 12:08
Selected for GREAT 2016: None
Selected for GREAT 2017: GREAT a
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/16956

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